If you work with a lot of data daily, you may be familiar with Azure Data Factory (ADF). Users may combine data from numerous sources using this service. Data must be processed to be arranged and made usable, and it is a laborious and expensive effort to work on all this data to offer a consistent data flow. Azure Data Factory comes into play to deal with data. To process the work, you must get an Azure Data Factory certification.
Read on to learn about the course and its perks.
What Is Azure Data Factory?
The fully managed serverless Azure Data Factory (ADF) is a solution for collecting, preparing, and converting all the data at a large scale. It makes it possible for every company, regardless of sector, to utilize it for analytics, data engineering, operational data integration, feeding data into data warehouses, and more. For example, if you have several SQL Server Integration Services (SSIS) packages, Azure Data Factory will run these SSIS packages as-is (consisting of custom SSIS components). This helps with using Azure Data Factory by any developer for business data integration requirements. Also, look for some of the other Microsoft certification courses, as they may be helpful if you find ADF demanding.
How Does ADF Work?
You can build data pipelines that transfer and transform data and then schedule their execution with the help of the Data Factory service. As a result, time-sliced data is used and created by processes. We can set the pipeline mode as expected (once per day) or one time. Data-driven strategies in Azure Data Factory generally consist of three phases.
- Connect and Gather:
Establish connections to all necessary data and processing sources, including SaaS, FTP, file shares, and online services. Then, use the Copy Activity in a data pipeline to transport data from both cloud source data stores and on-premise to a centralization data repository in the cloud for further processing. Then, move the data to the central location for further processing.
- Adapt and Improve:
Data is converted utilizing computing services like Spark, Azure Data Lake Analytics, HDInsight Hadoop, and machine learning once it is in a consolidated data repository in the cloud.
- Publish:
Deliver converted data from your cloud storage sources to on-premise providers like SQL Server or retain it for use by BI and analytics tools and other programs.
Azure Data Factory Course Prerequisites
The Azure Data Factory certification course requires several sets of things to be eligible. The issues associated with the design and execution of data lakes, data warehouses, stream analytics, and factories in Azure SQL Data Warehouse and Azure Data Factory should be familiar to participants who intend to enroll in Microsoft Azure Data Factory training.
- A necessary skill set is the capacity to use corporate intelligence efficiently.
- You should be accustomed to using PowerShell with Azure.
- Understanding of Hive, Pig, and JSON programming.
- Several years of expertise in data integration.
- Years of expertise with Azure DBaaS platforms.
Who Can Enroll In The Certification Course For Microsoft Azure Data Factory?
The following individuals can enroll in online Microsoft Azure Data Factory training:
- Data Architects and Engineers
- Data Integration Engineers
- IT Professionals
- ETL Developers
- Data Analysts
- Data Management Professionals
- Database Managers
- Database Developers
- SQL Database Professionals
- Aspiring Database Professionals
- Professionals who try for the DP-203 certification exam
- Professionals interested in Microsoft Azure Data Factory
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Microsoft ADF Course Benefits:
Along with investigating ADF certification, you can also look for other Microsoft certification courses. Here are some ADF course benefits.
- Microsoft ADF Certification training for 5 days with instructors.
- Open access to the Microsoft ADF course preview.
- Materials for Microsoft Accredited ADF courses created by SMEs Expert Microsoft Azure teachers with experience across various global industrial sectors.
- Utilize Microsoft Azure Data workouts and lab sessions to gain practical experience.
- Well-known certificate of completion for Microsoft ADF training program.
- Training for Microsoft ADF is offered in more than 100 locations worldwide.
Main ADF Components:
Understanding the characteristics of Azure Data Factory is crucial to comprehend how it operates. The main components are explained below.
- Datasets: Data source configuration settings are present in datasets but at a more detailed level. A dataset has both a structure and the name of a table or file. In addition, each dataset has a specific connected service, establishing the set of potential dataset characteristics.
- Activities: Activities include data transfers, control flow processes, and transformations. Activity configurations provide choices for database queries, stored procedure names, parameters, and script locations. One or more input datasets and one or more output datasets are both possible for an activity.
- Linked Services: This includes configuration data for specific data sources. This might contain details like the server’s or database’s name, file location, login credentials, and more. According to the nature of the work, each data flow may include one or more associated services.
- Pipelines: Pipelines are collections of rational activities. A data factory’s pipelines can each have one or even more actions. As a result, pipelines greatly simplify the scheduling and monitoring of several logically connected processes.
- Triggers: Triggers are setups for pipeline scheduling that include configuration parameters like execution frequency, start/end dates, and more. Triggers are only necessary if you want pipelines to operate automatically and according to a predetermined schedule; they are not required for ADF implementation.
Bottom Line:
Data originating from many sources is crucial to the current state of information technology. Azure Data Factory is a terrific solution to help your data go more quickly onto the cloud in the real world. The cloud is our future. ADF may perform more intricate changes by executing PySpark code in Databricks, creating webhook callouts, and calling a unique virtual machine. Microsoft’s Azure Data Factory certification will help you achieve a consistent data flow and different processes to manage data efficiently.
